@article {1515,
	title = {Extraction of airways from CT (EXACT{\textquoteright}09).},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {31},
	year = {2012},
	month = {2012 Nov},
	pages = {2093-107},
	abstract = {This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74\% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.},
	keywords = {Algorithms, Analysis of Variance, Databases, Factual, Humans, Lung, Radiographic Image Enhancement, Tomography, X-Ray Computed, Trachea},
	issn = {1558-254X},
	doi = {10.1109/TMI.2012.2209674},
	author = {Lo, Pechin and van Ginneken, Bram and Reinhardt, Joseph M and Yavarna, Tarunashree and de Jong, Pim A and Irving, Benjamin and Fetita, Catalin and Ortner, Margarete and R{\^o}mulo Pinho and Jan Sijbers and Feuerstein, Marco and Fabija{\'n}ska, Anna and Bauer, Christian and Beichel, Reinhard and Mendoza, Carlos S and Wiemker, Rafael and Lee, Jaesung and Reeves, Anthony P and Born, Silvia and Weinheimer, Oliver and van Rikxoort, Eva M and Tschirren, Juerg and Mori, Ken and Odry, Benjamin and Naidich, David P and Hartmann, Ieneke and Eric A. Hoffman and Prokop, Mathias and Pedersen, Jesper H and de Bruijne, Marleen}
}
@article {1313,
	title = {Force Feedback to Assist Active Contour Modelling for Tracheal Stenosis Segmentation},
	journal = {Advances in Human-Computer Interaction},
	volume = {2012},
	year = {2012},
	abstract = {Manual segmentation of structures for diagnosis and treatment of various diseases is a very time-consuming procedure. Therefore, some level of automation during the segmentation is desired, as it often significantly reduces the segmentation time. A typical solution is to allow manual interaction to steer the segmentation process, which is known as semiautomatic segmentation. In 2D, such interaction is usually achieved with click-and-drag operations, but in 3D a more sophisticated interface is called for. In this paper, we propose a semi-automatic Active Contour Modelling for the delineation of medical structures in 3D, tomographic images. Interaction is implemented with the employment of a 3D haptic device, which is used to steer the contour deformation towards the correct boundaries. In this way, valuable haptic feedback is provided about the 3D surface and its deformation. Experiments on simulated and real tracheal CT data showed that the proposed technique is an intuitive and effective segmentation mechanism.},
	doi = {doi:10.1155/2012/632498},
	url = {http://www.hindawi.com/journals/ahci/2012/632498/},
	author = {Lode Vanacken and R{\^o}mulo Pinho and Jan Sijbers and Karen Coninx}
}
@article {rpinhoTournoyjsijbers2011,
	title = {Assessment and Stenting of Tracheal Stenosis using Deformable Shape Models},
	journal = {Medical Image Analysis},
	volume = {15},
	number = {2},
	year = {2011},
	pages = {250-266},
	abstract = {This work presents a decision support system for the assessment of tracheal stenosis. In the proposed method, a statistical shape model of healthy tracheas is registered to a 3D CT image of a patient with tracheal stenosis. The registration yields an estimation of the shape of the patient{\textquoteright}s trachea as if stenosis was not present. From this point, the extent and the severity of the stenosis is assessed and stent parameters are obtained automatically. The method was extensively evaluated on simulation as well on real data and the results showed that it is accurate and fast enough to be used in the clinical setting.},
	issn = {1361-8423},
	doi = {http://dx.doi.org/doi:10.1016/j.media.2010.12.001},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Jan Sijbers}
}
@inbook {rpinhoTournoyjsijbers2010,
	title = {Computer-Aided Assessment and Stenting of Tracheal Stenosis},
	booktitle = {Computer Aided Diagnosis of Lung Imaging},
	year = {2010},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Jan Sijbers},
	editor = {Ayman El-Baz}
}
@mastersthesis {1284,
	title = {A Decision Support System for the Assessment and Stenting of Tracheal Stenosis},
	year = {2010},
	month = {18/11/2010},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {This thesis sets forth a decision support system that proposes a method for automatic assessment of tracheal stenosis and prediction of stent length and diameter from chest CT scans. The main idea behind the proposed method is to estimate the shape of the trachea of a patient as if stenosis were not present. This shape can be used the by the physician for surgery planning and is the basis for the automatic assessment of the stenosis and the prediction of patient-specific stents.
},
	author = {R{\^o}mulo Pinho}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2010,
	title = {A Decision Support System for the Treatment of Tracheal Stenosis},
	booktitle = {Proc. of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)},
	year = {2010},
	month = {August},
	pages = {72-76},
	publisher = {IAPR},
	organization = {IAPR},
	address = {Istanbul},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@inproceedings {rpinhoTournoyGosselinjsijbers2009,
	title = {Assessment of Tracheal Stenosis Using Active Shape Models of Healthy Tracheas: A Surface Registration Study},
	booktitle = {Proceedings of 2nd International Workshop on Pulmonary Image Analysis},
	year = {2009},
	month = {September},
	pages = {125-136},
	author = {R{\^o}mulo Pinho and Kurt G. Tournoy and Robert Gosselin and Jan Sijbers}
}
@conference {BernatthuysmanGlabbeekjsijbersrpinhoGielen2009,
	title = {Exploring the Clavicle: Morphometric Differences Using a 3D Model},
	year = {2009},
	month = {February},
	address = {Las Vegas,Nevada},
	author = {Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and R{\^o}mulo Pinho and Jan L Gielen}
}
@article {1614,
	title = {Osteologic exploration of the clavicle: a new approach},
	journal = {The FASEB Journal},
	volume = {23},
	year = {2009},
	edition = { (1_MeetingAbstracts)},
	chapter = {LB9},
	abstract = {Introduction Clavicles have a complex osteologic structure which makes a morphometric analysis extremely difficult. Our analysis shows the exact measurements and variations of the clavicle.

Materials and Methods 90 clavicles were dissected, CAT scanned and reconstructed. All measurements were automatically performed. The length and curvatures were calculated around the central line and for each cross-section the average, sagittal and axial diameter was calculated.

Results The average length is 163{\textpm}11 mm. For the length, there is a 9\% difference between the gender and 1.2\% between the left and right clavicle. Between the genders there is a volume difference of 36\%. The extremities show the biggest diameter, this decrease as approaching to the inflexion point, which is the smallest average diameter. In the axial view the acromial curvature is shorter and more curved than the sternal one. In males, the maximum acromial curvature has a difference of 18\% and the maximum sternal curvature a difference of 4\% compared with the females. In the coronal view there is a concave curvature with a maximum of 6 mm. In females the acromial end bends more posteroinferiorly.

Discussion This is the first 3D analysis performed on the clavicle. Females have a smaller clavicle with a shorter and less curved acromial curvature and a posteroinferior bending. The right clavicle is slightly shorter, thicker and more robust.},
	author = {Hilde Elisa Bortier and Amit Bernat and Toon Huysmans and Francis Van Glabbeek and Jan Sijbers and R{\^o}mulo Pinho and Gielen, Jan and Guy Hubens}
}
@inproceedings {rpinhoLuyckxjsijbers2009,
	title = {Robust Region Growing Based Intrathoracic Airway Tree Segmentation},
	booktitle = {Proceedings of 2nd International Workshop on Pulmonary Image Analysis},
	year = {2009},
	month = {September},
	pages = {261-271},
	author = {R{\^o}mulo Pinho and Sten Luyckx and Jan Sijbers}
}
@conference {thuysmanBernatrpinhojsijbersGlabbeeckParizelBortier2008,
	title = {A Framework for Morphometric Analysis of Long Bones: Application to the Human Clavicle},
	year = {2008},
	month = {March},
	author = {Toon Huysmans and Amit Bernat and R{\^o}mulo Pinho and Jan Sijbers and Francis Van Glabbeek and Paul M Parizel and H. Bortier}
}
@inproceedings {rpinhoBatenburgjsijbers2008,
	title = {Seeing Through the Window: Pre-fetching Strategies for Out-of-core Image Processing Algorithms},
	booktitle = {Proceedings of SPIE Medical Imaging},
	volume = {6919},
	year = {2008},
	month = {February},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, CA, USA},
	doi = {doi:10.1117/12.769423},
	author = {R{\^o}mulo Pinho and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {rpinhothuysmanVosjsijbers2008,
	title = {Tracheal Stent Prediction Using Statistical Deformable Models of Tubular Shapes},
	booktitle = {Proceedings of SPIE Medical Imaging},
	year = {2008},
	month = {February},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, CA, USA},
	doi = {http://dx.doi.org/10.1117/12.770237},
	author = {R{\^o}mulo Pinho and Toon Huysmans and W. Vos and Jan Sijbers}
}
@inproceedings {rpinhothuysmanVosjsijbers2008,
	title = {Tracheal Stent Prediction Using Statistical Deformable Models of Healthy Tracheas},
	booktitle = {Liege Image Days 2008: Medical Imaging},
	year = {2008},
	month = {March},
	author = {R{\^o}mulo Pinho and Toon Huysmans and W. Vos and Jan Sijbers}
}
@inproceedings {rpinhojsijbersthuysman2007,
	title = {Segmentation of The Human Trachea Using Deformable Statistical Models of Tubular Shapes},
	booktitle = {Proceedings of Advanced Concepts for Intelligent Vision Systems},
	series = {Lecture Notes in Computer Science},
	volume = {4678},
	year = {2007},
	month = {August},
	pages = {531-542},
	doi = {http://dx.doi.org/10.1007/978-3-540-74607-2_48},
	author = {R{\^o}mulo Pinho and Jan Sijbers and Toon Huysmans}
}
@inproceedings {rpinhojsijbersVos2006,
	title = {Efficient approaches to intrathoracic airway tree segmentations},
	booktitle = {Proceedings of the Biomedical Engineering IEEE/EMBS Benelux Symposium},
	volume = {2},
	year = {2006},
	month = {December},
	pages = {151-154},
	address = {Brussels, Belgium},
	author = {R{\^o}mulo Pinho and Jan Sijbers and W. Vos}
}
